Heterogeneity - Not a Verdict. Curing Content Without Pain or Costly Remedies

22 July 2025

Today, companies are actively implementing AI systems to automate and accelerate content creation processes, including for critically important product cards. However, this widespread and often decentralized use of AI leads to a new and serious problem: content heterogeneity. Differences in the AI systems used, prompts, and, most importantly, the diverse business objectives of various company departments create complexities that can negate the benefits of AI, leading to operational, managerial, and reputational costs.

Heterogeneity — Not a Verdict

Roots of Heterogeneity. Why AI-Generated Content Becomes Disparate

Content heterogeneity arises from the multifaceted interaction of various elements:

Diversity of AI Systems. Companies may use a wide range of tools:

Generative Large Language Models (LLMs) (e.g., GPT, Llama, Gemini) - for descriptions, headings, FAQs.
Image and Video Generators (Midjourney, DALL-E, Stable Diffusion, RunwayML) - for visual content.
AI Translators - for content localization.
Specialized E-commerce AI Solutions - for summarization, personalization, or A/B testing. Each of these systems has its own features, output style, and training data, which in itself can lead to differences in content.

Multitude of Prompt Approaches. Even when using the same AI model, variations in prompts have a huge impact:

Detailing: From general to highly specific instructions.
Style and Tone: Prompts may require a formal, sales-oriented, informative, friendly, or humorous tone.
Target Audience: Targeting different customer segments.
Structure: Requirements for format (lists, paragraphs, tables).
AI's "Role": Asking the model to act as an expert, marketer, or copywriter. Different employees or departments will use different prompts, directly leading to content disparity.

Business Objectives Across Departments:

Marketing Department

Primary Goal: Attracting attention, increasing brand awareness, stimulating interest, generating leads and traffic.
Impact on Content:

Focus on unique advantages and emotional delivery. Marketers will strive to use AI to create vivid, catchy headlines and descriptions that evoke emotions and highlight benefits, not just features.
Emphasis on brand style and tone. Prompts will aim for strict adherence to the brand book, a specific voice and intonation (e.g., friendly, expert, luxurious).
Optimization for advertising campaigns. Creating content that easily adapts to different ad formats and A/B tests.
Creative generation. Using AI to create banners, video snippets, images for social media, which are then used in product cards.

Sales / E-commerce Department

Primary Goal: Maximizing conversion, reducing buyer objections, increasing average check.
Impact on Content:

Focus on persuasive elements. Using AI to generate clear calls to action, answers to frequently asked questions, highlighting purchase benefits, creating a sense of urgency.
Detail and accuracy. Content must be as complete and accurate as possible to minimize returns and support inquiries. Prompts will require specific characteristics, sizes, and configurations.
Search Optimization (SEO). Incorporating keywords into descriptions and headings to improve visibility in search engines and marketplaces.
Competitor comparison. AI can be used to highlight product advantages over competitors.

Product / Product Development Department

Primary Goal: Accurate and complete presentation of technical specifications, product functionality, and features.
Impact on Content:

Focus on technical details. Creating precise specifications, feature lists, compatibility, standards. Using AI to structure complex technical information.
Clarity and unambiguousness. Content must be extremely clear for engineers, technical specialists, as well as for buyers who value details. Ambiguity should be avoided.
Information updates. Regular updates of product cards in accordance with product changes.

Logistics and Warehouse Accounting Department

Primary Goal: Providing information on delivery, package dimensions, weight, storage conditions.
Impact on Content:

Focus on delivery parameters. Using AI to generate information about dimensions, weight, transportation conditions, which are important for both the buyer and internal logistics.
Package contents information. Specifying what is included in the package for correct order assembly.

Customer Service / Support Department

Primary Goal: Reducing the number of inquiries, providing complete information for customers to resolve issues independently.
Impact on Content:

Focus on Frequently Asked Questions (FAQ). AI can be used to generate FAQ sections based on typical customer inquiries.
Clear instructions. Creating simple and understandable instructions for use, assembly, and care.

Thus, even if all departments use, say, GPT-4, the prompts they give and the expectations for the result will differ. A marketer will ask to "create an emotional description selling a dream," a salesperson - "list 5 key benefits for quick conversion," and a product specialist - "describe technical characteristics in detail." The result will be three different descriptions of the same product, each "correct" from the perspective of an individual employee, but collectively they will create heterogeneity.

This leads to the first signs of problems:

Inconsistent messaging. Consumers may encounter contradictory or differently emphasized information at various stages of the sales funnel or on different platforms.
Duplication of efforts. Several departments may generate similar content without knowing about their colleagues' work.
Aggregation complexity. How to assemble all these disparate, yet "correct," pieces of content into one cohesive and effective product card?

As can be seen, differences in departmental goals and focuses are a fundamental cause of heterogeneity.

Organizational Complexities. The Cost of Heterogeneity

Uncontrolled heterogeneity of AI-generated content poses a number of serious challenges for an organization:

Operational complexities and increased labor costs:

Unification and correction. Enormous volumes of manual work to bring disparate content to a single style, tone, and factual accuracy.
Quality control. An extremely complex and expensive process of verifying the reliability and consistency of information in large volumes of AI-generated content.
Increased iterations. Long cycles of approvals and revisions between departments due to version inconsistencies.
Risk of errors. High probability of contradictory or incorrect information appearing, leading to negative customer experience and returns.

Communication and coordination problems between departments:

Lack of a single source of truth. Confusion and disagreements about which product information is current and reliable.
Duplication of work. Redundant efforts in content generation by different teams, inefficient use of AI resources.
Conflicts of interest. Divergent departmental priorities can slow down processes and create internal friction.

Challenges in data management and AI infrastructure:

Content fragmentation. Storing data in disparate systems, making it difficult to search, update, and aggregate.
Integration complexities. The need for complex integration between various AI tools and internal company systems.
Prompt and model management. Lack of a centralized library of "golden prompts" and versioning of AI models.
Cost inefficiency. Uncontrolled expenses for API requests and computing power due to task duplication.

Impact on brand and user experience:

Inconsistent brand image. Contradictory content undermines a unified brand perception, reducing its professionalism and trust.
Worsened user experience. Confusing or incomplete information leads to customer frustration and reduced conversion.
Legal risks. Errors in descriptions can have serious legal consequences.

Centralized Solution for Content Harmonization

A key tool in combating content heterogeneity is the implementation of a PIM (Product Information Management) system. PIM serves as the "single source of truth" for all product information, centralizing, standardizing, and enriching data.

Centralization and single source of truth. PIM collects product information from all internal and external sources, eliminating fragmentation and ensuring all departments access one, up-to-date version of the data.
Structuring and standardization. PIM allows for defining strict attributes, categories, hierarchies, and templates for content. AI-generated content is integrated into these predefined structures, ensuring uniformity and simplifying validation.
Quality control and enrichment. PIM provides tools for data validation, automatic rule compliance checks, and collaborative content enrichment by various teams. AI-generated content passes through PIM, where it can be edited and refined by a human.
Version management and localization. PIM supports data versioning and multi-language capabilities, ensuring content consistency across different languages and the ability to track all changes.
Automated publication and distribution. PIM automates the export of verified and unified data to various channels, guaranteeing that current and consistent information is presented on all platforms.

Adaptation and Distribution for Multichannel Business

For e-commerce, PIM systems focused on distributing content across multiple external trading platforms are particularly valuable. They become a critically important link in combating heterogeneity:

Channel-specific content transformations. PIM MarketProvider allows for creating unique versions of content for each channel (e.g., Amazon, Shopify, Public Catalog, own website) based on a single "master version" from PIM. This eliminates the need to manually rewrite content for each platform's requirements.
Marketplace requirements management. The system contains pre-configured templates and export rules for popular marketplaces, simplifying the publication process and ensuring compliance with their specific requirements.
AI integration at the publication stage. AI can be used not only for initial content generation but also for its adaptation and optimization. It can automatically shorten descriptions to the required character limit, add or remove keywords for specific platform SEO, or perform translations. This allows for maintaining centralized control over content quality and accuracy while automating its adaptation.

Challenges of PIM Implementation and Synergy with AI

Despite the obvious advantages, implementing a PIM system, especially in the context of AI-generated content, comes with certain challenges:

Initial data population and migration. This is a large-scale project requiring careful planning and resources.
Defining "master data" and "generated data." It is necessary to clearly distinguish what information is fundamental (and strictly controlled by PIM) and what can be dynamically generated by AI (and adapted under PIM's control).
Prompt management. PIM can be extended to store and version "golden prompts" associated with specific product attributes, ensuring consistency of instructions for AI.
Changing workflows and staff training. Successful PIM implementation requires restructuring cross-functional workflows and training employees across all departments on new approaches to content creation, management, and publication, including interaction with AI.

Content heterogeneity, arising from the decentralized use of artificial intelligence for producing product cards, is a serious challenge for modern companies. It leads to operational costs, communication barriers, infrastructure complexities, and, most importantly, negatively impacts brand perception and user experience.

A PIM system, particularly PIM MarketProvider, represents not just a tool, but a strategic solution for overcoming this heterogeneity. It ensures the centralization, standardization, and quality control of product information, creating a "single source of truth." The integration of AI tools with PIM allows for automating and optimizing content creation at all stages - from initial generation to adaptation for specific channels.

Implementing PIM requires significant investment and effort in process transformation, but ultimately, it provides companies with the necessary structure and flexibility to scale the production of high-quality, uniform, and adapted content. Only such a systemic approach will allow for effectively leveraging the power of artificial intelligence, minimizing the risks of heterogeneity, and ensuring a competitive advantage in the e-commerce market.